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GNPSum: A code summarization enhancement framework based on Graph Node Position
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AI
DOI：10.1016/j.infsof.2025.107837.png)
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Abstract
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Code summarization is essential for effectively communicating a code’s core functionality and logic, enhancing software development efficiency, collaboration, and code quality. Traditional work has focused on generating summaries from textual information extracted from the source code. However, these approaches often fail to capture the hierarchical structure critical for effective summarization. To effectively capture the hierarchical structure of the code, which is crucial for accurate summarization, researchers often integrate structural elements such as Syntax Trees (AST) into their models. However, conventional embedding methods struggle to accurately discern the semantic nuances within the code, particularly for nodes with similar content but distinct structural roles. The relative positional information of these nodes, which often conveys semantics absent from the source code itself, is frequently overlooked, limiting the model’s ability to fully exploit the hierarchical and contextual richness inherent in the code structure.
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